Evidence map›Paper›PMID 39478636›Full record

ArticleGenome biology2024

Winsorization greatly reduces false positives by popular differential expression methods when analyzing human population samples.

Lu Yang, Xianyang Zhang, Jun Chen

Abstract readComment
In one paragraph

Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Lu YangDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA.
Xianyang ZhangDepartment of Statistics, Texas A and M University, College Station, TX, 77843, USA.
Jun ChenDivision of Computational Biology, Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, 55905, USA. chen.jun2@mayo.edu.ORCID 0000-0002-1273-5624

Funding

Methods for microbiome compositional dataR01GM144351 · NIGMS · MAYO CLINIC ROCHESTER · PI Jun Chen, Xianyang Zhang · 2022 to 2026
$1.6M
Methods for Analysis of Genomic Data with Auxiliary InformationR21HG011662 · NHGRI · MAYO CLINIC ROCHESTER · PI CHEN, JUN, ZHANG, XIANYANG · 2021 to 2022
$434k
NHGRI NIH HHS R21 HG011662NIGMS NIH HHS R01 GM144351NIGMS NIH HHS R01GM144351
6 · The paper itself

Abstract

A recent study found severely inflated type I error rates for DESeq2 and edgeR, two dominant tools used for differential expression analysis of RNA-seq data. Here, we show that by properly addressing the outliers in the RNA-Seq data using winsorization, the type I error rate of DESeq2 and edgeR can be substantially reduced, and the power is comparable to Wilcoxon rank-sum test for large datasets. Therefore, as an alternative to Wilcoxon rank-sum test, they may still be applied for differential expression analysis of large RNA-Seq datasets.

Indexed as

Gene Expression ProfilingFalse Positive ReactionsHumansRNA-SeqSequence Analysis, RNASoftware

Identifiers

PMID39478636
PMCPMC11523781

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.